Models representing data relationships and patterns, such as functions, algorithms, systems, and the like, may be used to generate output that corresponds to input in some way. In a common scenario, a set of data may be obtained, and a model of a theoretical function that generated the set of data may be sought. The function may be considered theoretical because the set of data may be obtained from a complex real world system, rather than from a well-defined function. For example, the data may include measurements related to the operation of a particular system, and it may be desirable to predict values for the measurements most likely to occur given certain input criteria. Various techniques may be used to model the theoretical function that best fits the set of data.
One technique for modeling sets of data is referred to as maximum likelihood estimation. Maximum likelihood estimation (MLE) is a statistical technique to determine, for a particular model, which parameters of the model are most likely to characterize a given set of data. For example, a set of data may appear to have a normal distribution. MLE can be used to determine the parameters (e.g., the mean and standard deviation) of the normal distribution that best characterizes the set of data. The determined parameters can then be used to predict which values are likely to occur given certain input.